Simplifying Big Data Applications with Apache Spark 2.0 - Databricks

Simplifying Big Data Applications with Apache Spark 2.0

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Apache Spark 2.0 was released this summer and is already being widely adopted. I’ll talk about how changes in the API have made it easier to write batch, streaming and realtime applications. The Dataset API, which is now integrated with DataFrames, makes it possible to benefit from powerful optimizations such as pushing queries into data sources, while the Structured Streaming extension to this API makes it possible to run many of the same computations in a streaming fashion automatically.

About Matei Zaharia

Matei Zaharia is an assistant professor of computer science at Stanford University and Chief Technologist at Databricks. He started the Spark project during his PhD at UC Berkeley in 2009. Before that, Matei worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. Matei’s research was recognized through the 2014 ACM Doctoral Dissertation Award for the best PhD dissertation in computer science.